Matching an object to be displayed involves a matching degree between a search keyword (also referred to as query) selected by a user and product information to be released or promoted. Existing technologies usually adopt a linear model constructed by text correlation features to calculate a score of correlation between the search keyword and the released or promoted product information. The matching degree between the search keyword and the released product information is determined based on the correlation score, and the user is recommended to select a search keyword having a high matching degree. The calculation of the correlation score includes firstly assigning a weight to each type of text correlation features to indicate a degree of importance of each type of the text correlation features. The text correlation features include correlation features such as a rate of matching between the search keyword and a title of the released product information, a position and an ordering of a term extracted from the search keyword within the title of the released product information, etc. Secondly, values of the text correlation features are labeled for samples of search keyword and released product information pairs (i.e., query and offer pairs) using human experience, and all search keyword and released product information pairs are labeled by referencing the labeled samples. Thirdly, a linear model s=ΣiIwi*fi is used to calculate the correlation score for the search keyword and the released product information, where wi is the weight of each type of the text correlation features, fi is a value of each type of the text correlation features, I represents a feature space, and s is the correlation score for the search keyword and the released product information.
However, due to the excessively large number of search keyword and released product information pairs in reality, a large amount of system resources are consumed for labeling text correlation feature values thereof. Moreover, the number of samples that are labeled using human experience is limited, and labeling all the search keyword and released product information pairs based on the labeled samples is impossible. Furthermore, an accuracy of labeling the values of the text correlation features for the samples using human experience is very low. In addition, in an event that a system maintenance personnel is replaced, the number of text correlation features is increased or decreased and an associated system is upgraded, the values of the text correlation features need to be relabeled, thus resulting in a high maintenance cost. When complaints are received from customers, labeling the values of the text correlation features using human experience cannot be used as an effective explanation to the customers.
Therefore, how to accurately and conveniently determine a degree of matching between a search keyword and released product information has become a technical problem that needs to be solved.